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Assessment System for Child Head Injury from Falls Based on Neural Network Learning
Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534444/ https://www.ncbi.nlm.nih.gov/pubmed/37765953 http://dx.doi.org/10.3390/s23187896 |
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author | Yang, Ziqian Tsui, Baiyu Wu, Zhihui |
author_facet | Yang, Ziqian Tsui, Baiyu Wu, Zhihui |
author_sort | Yang, Ziqian |
collection | PubMed |
description | Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven. |
format | Online Article Text |
id | pubmed-10534444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105344442023-09-29 Assessment System for Child Head Injury from Falls Based on Neural Network Learning Yang, Ziqian Tsui, Baiyu Wu, Zhihui Sensors (Basel) Article Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven. MDPI 2023-09-15 /pmc/articles/PMC10534444/ /pubmed/37765953 http://dx.doi.org/10.3390/s23187896 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Ziqian Tsui, Baiyu Wu, Zhihui Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title_full | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title_fullStr | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title_full_unstemmed | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title_short | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
title_sort | assessment system for child head injury from falls based on neural network learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534444/ https://www.ncbi.nlm.nih.gov/pubmed/37765953 http://dx.doi.org/10.3390/s23187896 |
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